deprecate sole AR/NAR model by only keeping the AR+NAR (the beauty of no one using this is that I can break compat as much as I want), add tone token for when I classify my dataset with tone/emotion in the future, some other things
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README.md
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README.md
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@ -6,16 +6,10 @@
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An unofficial PyTorch implementation of [VALL-E](https://valle-demo.github.io/), utilizing the [EnCodec](https://github.com/facebookresearch/encodec) encoder/decoder.
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[Main Repo](https://git.ecker.tech/mrq/vall-e) | [GitHub Mirror](https://github.com/e-c-k-e-r/vall-e/)
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> **Note** Development on this is very sporadic. Gomen.
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## Requirements
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* [`DeepSpeed`](https://github.com/microsoft/DeepSpeed#requirements):
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- DeepSpeed training is Linux only. Installation under Windows should ignore trying to install DeepSpeed.
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- If your config YAML has the training backend set to `deepspeed`, you will need to have a GPU that DeepSpeed has developed and tested against, as well as a CUDA or ROCm compiler pre-installed to install this package.
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* [`espeak-ng`](https://github.com/espeak-ng/espeak-ng/):
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- For phonemizing text, this repo requires `espeak`/`espeak-ng` installed.
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- Linux users can consult their package managers on installing `espeak`/`espeak-ng`.
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@ -24,7 +18,7 @@ An unofficial PyTorch implementation of [VALL-E](https://valle-demo.github.io/),
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## Install
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Simply run `pip install git+https://git.ecker.tech/mrq/vall-e`.
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Simply run `pip install git+https://git.ecker.tech/mrq/vall-e` or `pip install git+https://github.com/e-c-k-e-r/vall-e`.
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I've tested this repo under Python versions `3.10.9` and `3.11.3`.
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@ -68,7 +62,7 @@ A script to setup a proper environment and train can be invoked with `./scripts/
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If you're interested in creating an HDF5 copy of your dataset, simply invoke: `python -m vall_e.data --action='hdf5' yaml='./data/config.yaml'`
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5. Train the AR and NAR models using the following scripts: `python -m vall_e.train yaml=./data/config.yaml`
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5. Train the model using the following scripts: `python -m vall_e.train yaml=./data/config.yaml`
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* If distributing your training (for example, multi-GPU), use `deepspeed --module vall_e.train yaml="./data/config.yaml"`
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You may quit your training any time by just entering `quit` in your CLI. The latest checkpoint will be automatically saved.
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@ -93,18 +87,23 @@ You can specify what X and Y labels you want to plot against by passing `--xs to
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#### Training Under Windows
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As training under `deepspeed` and Windows is not supported, under your `config.yaml`, simply change `trainer.backend` to `local` to use the local training backend.
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As training under `deepspeed` and Windows is not (easily) supported, under your `config.yaml`, simply change `trainer.backend` to `local` to use the local training backend.
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Keep in mind that creature comforts like distributed training or `float16` training cannot be verified as working at the moment.
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Keep in mind that creature comforts like distributed training or `float16` training cannot be verified as working at the moment with the local trainer.
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#### Training on Low-VRAM Cards
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During experimentation, I've found I can comfortably train on a 4070Ti (12GiB VRAM) with `trainer.deepspeed.compression_training` enabled with both the AR and NAR at a batch size of 16, albeit I feel this is mostly snakeoil. Better VRAM savings can be had with use of BitsAndBytes and their respective flags (specifically its AdamW implementation).
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VRAM use is also predicated on your dataset; a mix of large and small utterances will cause VRAM usage to spike and can trigger OOM conditions during the backwards pass if you are not careful.
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During experimentation, I've found I can comfortably train on a 4070Ti (12GiB VRAM). Howver, VRAM use is predicated on your dataset; a mix of large and small utterances will cause VRAM usage to spike and can trigger OOM conditions during the backwards pass if you are not careful.
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Additionally, under Windows, I managed to finetune the AR on my 2060 (6GiB VRAM) with a batch size of 8 (although, with the card as a secondary GPU).
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#### Training Caveats
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Unfortunately, efforts to train a *good* foundational model seems entirely predicated on a good dataset. My dataset might be too fouled with:
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* too short utterances: trying to extrapolate longer contexts seems to utterly fall apart from just the `text` being too long.
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* too tightly trimmed utterances: there being little to no space at the start and end might harm associating `<s>` and `</s>` tokens with empty utterances.
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* a poorly mapped phoneme mapping: I naively crafted my own phoneme mapping, where a HuggingFace tokenizer might supply a better token mapping.
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#### Backend Architectures
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As the core of VALL-E makes use of a language model, various LLM architectures can be supported and slotted in. Currently supported:
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@ -112,6 +111,8 @@ As the core of VALL-E makes use of a language model, various LLM architectures c
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* `transformer`: a basic attention-based transformer implementation, with attention heads + feed forwards.
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* `retnet`: using [TorchScale's RetNet](https://github.com/microsoft/torchscale/blob/main/torchscale/architecture/retnet.py) implementation, a retention-based approach can be used instead.
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- Its implementation for MoE can also be utilized.
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* `retnet-hf`: using [syncdoth/RetNet/](https://github.com/syncdoth/RetNet/) with a HuggingFace-compatible RetNet model
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- inferencing cost is about 0.5x, and MoE is not implemented.
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* `llama`: using HF transformer's LLaMa implementation for its attention-based transformer, boasting RoPE and other improvements.
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* `mixtral`: using HF transformer's Mixtral implementation for its attention-based transformer, also utilizing its MoE implementation.
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* `bitnet`: using [this](https://github.com/kyegomez/BitNet/) implementation of BitNet's transformer.
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@ -121,11 +122,11 @@ As the core of VALL-E makes use of a language model, various LLM architectures c
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To export the models, run: `python -m vall_e.export yaml=./data/config.yaml`.
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This will export the latest checkpoints, for example, under `./data/ckpt/ar-retnet-2/fp32.pth` and `./data/ckpt/nar-retnet-2/fp32.pth`, to be loaded on any system with PyTorch, and will include additional metadata, such as the symmap used, and training stats.
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This will export the latest checkpoints, for example, under `./data/ckpt/ar+nar-retnet-8/fp32.pth`, to be loaded on any system with PyTorch, and will include additional metadata, such as the symmap used, and training stats.
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## Synthesis
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To synthesize speech, invoke either (if exported the models): `python -m vall_e <text> <ref_path> <out_path> --ar-ckpt ./models/ar.pt --nar-ckpt ./models/nar.pt` or `python -m vall_e <text> <ref_path> <out_path> yaml=<yaml_path>`
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To synthesize speech, invoke either (if exported the models): `python -m vall_e <text> <ref_path> <out_path> --model-ckpt ./data/ckpt/ar+nar-retnet-8/fp32.pth` or `python -m vall_e <text> <ref_path> <out_path> yaml=<yaml_path>`
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Some additional flags you can pass are:
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* `--language`: specifies the language for phonemizing the text, and helps guide inferencing when the model is trained against that language.
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@ -154,7 +155,6 @@ And some experimental sampling flags you can use too (your mileage will ***defin
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## To-Do
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* train and release a ***good*** model.
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- the current model seems to require a ***long*** time of training at a very small LR rate to try and cover a wide variety of speakers of varying acoustics.
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* clean up the README, and document, document, document onto the wiki.
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* extend to ~~multiple languages ([VALL-E X](https://arxiv.org/abs/2303.03926)) and~~ addditional tasks ([SpeechX](https://arxiv.org/abs/2308.06873)).
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- training additional tasks needs the SpeechX implementation to be reworked.
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@ -164,7 +164,7 @@ And some experimental sampling flags you can use too (your mileage will ***defin
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+ this requires a properly trained AR, however.
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* work around issues with extending context past what's trained (despite RetNet's retention allegedly being able to defeat this):
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- "sliding" AR input, such as have the context a fixed length.
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+ the model may need to be trained for this with a fancy positional embedding injected. Naively sliding the context window while making use of the RetNet implementation's positional embedding doesn't seem fruitful.
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+ the model may need to be trained for this with a fancy positional embedding injected OR already trained with a sliding context window in mind. Naively sliding the context window while making use of the RetNet implementation's positional embedding doesn't seem fruitful.
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## Notices and Citations
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@ -13,8 +13,7 @@ def main():
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parser.add_argument("--out-path", type=Path, default=None)
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parser.add_argument("--yaml", type=Path, default=None)
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parser.add_argument("--ar-ckpt", type=Path, default=None)
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parser.add_argument("--nar-ckpt", type=Path, default=None)
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parser.add_argument("--model-ckpt", type=Path, default=None)
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parser.add_argument("--max-ar-steps", type=int, default=6 * 75)
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parser.add_argument("--max-nar-levels", type=int, default=7)
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@ -41,7 +40,7 @@ def main():
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parser.add_argument("--dtype", type=str, default=None)
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args = parser.parse_args()
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tts = TTS( config=args.yaml, ar_ckpt=args.ar_ckpt, nar_ckpt=args.nar_ckpt, device=args.device, dtype=args.dtype, amp=args.amp )
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tts = TTS( config=args.yaml, model_ckpt=args.model_ckpt, device=args.device, dtype=args.dtype, amp=args.amp )
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tts.inference(
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text=args.text,
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references=args.references,
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@ -162,6 +162,9 @@ class Dataset:
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@dataclass()
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class Model:
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_max_levels: int = 0
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_embeddings: str | None = None
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name: str = "" # vanity name for the model
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version: int = 1 # 1 = old with MultiEmbedding, 2 = new with AudioEmbedding
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size: str | dict = "full" # preset string or explicitly defined dimensionality
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@ -169,6 +172,7 @@ class Model:
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prom_levels: int = 8 # RVQ-bin levels this model accepts as an input prompt
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tasks: int = 8 # ["tts", "ns", "sr", "tse", "cse", "nse"] and leaves two more for anything else I want (like "svc")
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langs: int = 1 # defined languages
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tones: int = 1 # defined tones
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experts: int = 1
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arch_type: str = "retnet" # or "transformer""
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training: bool = True # unneeded now
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p_ar_level: float | str = "auto" # determines odds of selecting the AR (level 0) when training, "auto" for default behavior
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frozen_params: list[str] = field(default_factory=lambda: []) # frozen parameters that are not updated when training
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def get(self, name=None):
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return [ self ] if not name or self.name == name else []
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@property
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def max_levels(self):
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return self._max_levels if self._max_levels > 0 else self.prom_levels
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@property
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# required for fp8 as the lengths needs to be divisible by 8
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def input_alignment(self):
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if self.interleave:
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name.append("interleaved")
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else:
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name.append(f'{cfg.models.prom_levels}')
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name.append(f'{cfg.model.prom_levels}')
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return "-".join(name)
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def activation_checkpointing(self):
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return cfg.trainer.activation_checkpointing
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@dataclass()
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class Models:
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_max_levels: int = 0
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_prom_levels: int = 1
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_embeddings: str | None = None
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_models: list[Model] = field(default_factory=lambda: [
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Model(name="ar", resp_levels=1, prom_levels=8, tasks=8, langs=1, experts=1, training=True, interleave=False),
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Model(name="nar", resp_levels=7, prom_levels=8, tasks=8, langs=1, experts=1, training=True, interleave=False),
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])
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def get(self, name=None):
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if not name:
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return [ Model(**model) for model in self._models ]
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for model in self._models:
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if model.name == name:
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return model
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raise ValueError
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@property
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def ar(self):
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return self.get("ar")
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@property
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def ar_nar(self):
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return self.get("ar+nar")
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@property
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def nar(self):
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return self.get("nar")
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@property
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def prom_levels(self):
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prom_levels = self._prom_levels
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for model in self._models:
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prom_levels = max(prom_levels, model.prom_levels)
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return prom_levels
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@property
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def tasks(self):
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tasks = 1
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for model in self._models:
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tasks = max(tasks, model.tasks)
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return tasks
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@property
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def max_levels(self):
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return self._max_levels if self._max_levels > 0 else self.prom_levels
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@dataclass()
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class Hyperparameters:
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batch_size: int = 8
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experimental: bool = False # So I can stop commenting out things when committing
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dataset: Dataset = field(default_factory=lambda: Dataset)
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models: Models = field(default_factory=lambda: Models)
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model: Model = field(default_factory=lambda: Model)
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hyperparameters: Hyperparameters = field(default_factory=lambda: Hyperparameters)
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evaluation: Evaluation = field(default_factory=lambda: Evaluation)
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trainer: Trainer = field(default_factory=lambda: Trainer)
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def format( self ):
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self.dataset = Dataset(**self.dataset)
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self.models = Models(**self.models)
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self.model = Model(**self.model)
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self.hyperparameters = Hyperparameters(**self.hyperparameters)
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self.evaluation = Evaluation(**self.evaluation)
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self.trainer = Trainer(**self.trainer)
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_logger = logging.getLogger(__name__)
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# to-do: clean up this symmap mess
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def get_phone_symmap():
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if cfg.dataset.use_hdf5 and 'symmap' in cfg.hdf5:
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return json.loads( cfg.hdf5['symmap'].asstr()[()] )
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symmap = {'<s>': 1, '</s>': 2, ' ': 3, '.': 4, ',': 5, '!': 6, '?': 7, 'p': 7, 'iː': 8, 'ɚ': 9, 'ˌ': 10, 'dˌ': 11, 'mˌ': 12, 'd': 13, 'ɹ': 14, 'tˈ': 15, 'pˌ': 16, 'uː': 17, 'l': 18, 'æ': 19, 'ɛ': 20, 'ɪ': 21, 'j': 22, 'ʊ': 23, 't': 24, 'n': 25, 'v': 26, 'a': 27, 'o': 28, 'ŋ': 29, 'w': 30, 'ʌ': 31, 'hˈ': 32, 'ɡˈ': 33, 'ə': 34, 'θˈ': 35, 'dˈ': 36, 'wˌ': 37, 'h': 38, 'z': 39, 'k': 40, 'ð': 41, 'ɡˌ': 42, 'ˈ': 43, 'fˈ': 44, 'i': 45, 's': 46, 'ʃ': 47, 'wˈ': 48, 'ðˈ': 49, 'ɹˈ': 50, 'lˈ': 51, 'ɡ': 52, 'oː': 53, 'mˈ': 54, 'e': 55, 'ɑː': 56, 'nˈ': 57, 'm': 58, 'θˌ': 59, 'sˈ': 60, 'f': 61, 'ɔː': 62, 'hˌ': 63, 'b': 64, 'jˈ': 65, 'ɐ': 66, 'ʒˈ': 67, 'θ': 68, 'bˈ': 69, 'ɾ': 70, 'ɜː': 71, 'ʌˈ': 72, 'ʃˌ': 73, 'bˌ': 74, 'kˈ': 75, 'ɔ': 76, 'zˈ': 77, 'ᵻ': 78, 'kˌ': 79, 'vˈ': 80, 'fˌ': 81, 'ʒ': 82, 'ʃˈ': 83, 'ɹˌ': 84, 'tˌ': 85, 'pˈ': 86, 'ðˌ': 87, 'sˌ': 88, 'nˌ': 89, 'lˌ': 90, '̩': 91, 'ʔ': 92, 'vˌ': 93, 'ɪˈ': 94, '"': 95, 'ɪˌ': 96, 'ʒˌ': 97, 'uːˌ': 98, 'ʊˈ': 99, 'jˌ': 100, 'uːˈ': 101, 'iːˈ': 102, 'zˌ': 103, '.ˈ': 104, '…': 105, 'ŋˌ': 106, 'ɐˌ': 107, '—ˈ': 108, 'iˌ': 109, 'iːˌ': 110, 'ɛː': 111, ')': 112, ')ˈ': 113, '(': 114, 'u': 115, '-': 116, 'ɖˈ': 117, 'iˈ': 118, 'ʰˈ': 119, 'ɟˈ': 120, '̃': 121, 'eː': 122, 'ɾˈ': 123, 'r': 124, 'ʰ': 125, '-ˌ': 126, 'ɫ': 127, 'q': 128, '—': 129, 'ʊˌ': 130, 'aː': 131, 'cˈ': 132, '…ˈ': 133, 'c': 134, 'ɳ': 135, 'ɐˈ': 136, 'x': 137, 'ʔˌ': 138, '.ˌ': 139, 'ɑ': 140, '?ˈ': 141, '̩ˈ': 142, '"ˈ': 143, ',ˈ': 144, 'ŋˈ': 145, 'əˌ': 146, '!ˈ': 147, '"ˌ': 148, '?ˌ': 149, ',ˌ': 150, '—ˌ': 151, '̩ˌ': 152, 'əˈ': 153, '!ˌ': 154, 'ɬ': 155, 'ʲ': 156, '¡': 157, 'ɯ': 158, 'qˌ': 159, 'ʑ': 160, 'ʑˈ': 161, '¿': 162, 'ɑːˈ': 163, 'iːː': 164, 'ɛˈ': 165, '¡ˈ': 166, 'æˈ': 167, 'ç': 168, 'ɾˌ': 169, 'ᵻˈ': 170, 'xˈ': 171, 'ɔːˈ': 172, ';': 173, 'ɬˌ': 174, ':': 175, 'ʔˈ': 176, 'ɑːˌ': 177, 'ɬˈ': 178, '”': 179, '“': 180, '“ˈ': 181, '“ˌ': 182, ';ˈ': 183, ';ˌ': 184, ':ˈ': 185, '1': 186, 'rˈ': 187, 'qˈ': 188, 'ᵻˌ': 189, 'ä': 190, '̞ˌ': 191, '̞': 192, 'ũˌ': 193, 'ʑˌ': 194, 'ᵝ': 195, 'ɽ': 196, 'ʲˌ': 197, 'ᵝˌ': 198, 'ũ': 199, 'ũˈ': 200, 'äˌ': 201, 'ɕ': 202, 'ɕˌ': 203, 'ɽˌ': 204, 'çˌ': 205, '…ˌ': 206, '̞ˈ': 207, 'äˈ': 208, 'ɽˈ': 209, 'ɸˌ': 210, 'ɴ': 211, 'ɸˈ': 212, 'ɕˈ': 213, 'ɸ': 214, 'ᵝˈ': 215, 'ʲˈ': 216, 'ĩ': 217, 'çˈ': 218, 'ĩˌ': 219, 'oˌ': 220, 'eˈ': 221, 'ʍ': 222, 'eˌ': 223, 'uˌ': 224, 'ʍˌ': 225, 'uˈ': 226, 'oˈ': 227, 'aˈ': 228}
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return symmap
|
||||
return {'<s>': 1, '</s>': 2, ' ': 3, '.': 4, ',': 5, '!': 6, '?': 7, 'p': 7, 'iː': 8, 'ɚ': 9, 'ˌ': 10, 'dˌ': 11, 'mˌ': 12, 'd': 13, 'ɹ': 14, 'tˈ': 15, 'pˌ': 16, 'uː': 17, 'l': 18, 'æ': 19, 'ɛ': 20, 'ɪ': 21, 'j': 22, 'ʊ': 23, 't': 24, 'n': 25, 'v': 26, 'a': 27, 'o': 28, 'ŋ': 29, 'w': 30, 'ʌ': 31, 'hˈ': 32, 'ɡˈ': 33, 'ə': 34, 'θˈ': 35, 'dˈ': 36, 'wˌ': 37, 'h': 38, 'z': 39, 'k': 40, 'ð': 41, 'ɡˌ': 42, 'ˈ': 43, 'fˈ': 44, 'i': 45, 's': 46, 'ʃ': 47, 'wˈ': 48, 'ðˈ': 49, 'ɹˈ': 50, 'lˈ': 51, 'ɡ': 52, 'oː': 53, 'mˈ': 54, 'e': 55, 'ɑː': 56, 'nˈ': 57, 'm': 58, 'θˌ': 59, 'sˈ': 60, 'f': 61, 'ɔː': 62, 'hˌ': 63, 'b': 64, 'jˈ': 65, 'ɐ': 66, 'ʒˈ': 67, 'θ': 68, 'bˈ': 69, 'ɾ': 70, 'ɜː': 71, 'ʌˈ': 72, 'ʃˌ': 73, 'bˌ': 74, 'kˈ': 75, 'ɔ': 76, 'zˈ': 77, 'ᵻ': 78, 'kˌ': 79, 'vˈ': 80, 'fˌ': 81, 'ʒ': 82, 'ʃˈ': 83, 'ɹˌ': 84, 'tˌ': 85, 'pˈ': 86, 'ðˌ': 87, 'sˌ': 88, 'nˌ': 89, 'lˌ': 90, '̩': 91, 'ʔ': 92, 'vˌ': 93, 'ɪˈ': 94, '"': 95, 'ɪˌ': 96, 'ʒˌ': 97, 'uːˌ': 98, 'ʊˈ': 99, 'jˌ': 100, 'uːˈ': 101, 'iːˈ': 102, 'zˌ': 103, '.ˈ': 104, '…': 105, 'ŋˌ': 106, 'ɐˌ': 107, '—ˈ': 108, 'iˌ': 109, 'iːˌ': 110, 'ɛː': 111, ')': 112, ')ˈ': 113, '(': 114, 'u': 115, '-': 116, 'ɖˈ': 117, 'iˈ': 118, 'ʰˈ': 119, 'ɟˈ': 120, '̃': 121, 'eː': 122, 'ɾˈ': 123, 'r': 124, 'ʰ': 125, '-ˌ': 126, 'ɫ': 127, 'q': 128, '—': 129, 'ʊˌ': 130, 'aː': 131, 'cˈ': 132, '…ˈ': 133, 'c': 134, 'ɳ': 135, 'ɐˈ': 136, 'x': 137, 'ʔˌ': 138, '.ˌ': 139, 'ɑ': 140, '?ˈ': 141, '̩ˈ': 142, '"ˈ': 143, ',ˈ': 144, 'ŋˈ': 145, 'əˌ': 146, '!ˈ': 147, '"ˌ': 148, '?ˌ': 149, ',ˌ': 150, '—ˌ': 151, '̩ˌ': 152, 'əˈ': 153, '!ˌ': 154, 'ɬ': 155, 'ʲ': 156, '¡': 157, 'ɯ': 158, 'qˌ': 159, 'ʑ': 160, 'ʑˈ': 161, '¿': 162, 'ɑːˈ': 163, 'iːː': 164, 'ɛˈ': 165, '¡ˈ': 166, 'æˈ': 167, 'ç': 168, 'ɾˌ': 169, 'ᵻˈ': 170, 'xˈ': 171, 'ɔːˈ': 172, ';': 173, 'ɬˌ': 174, ':': 175, 'ʔˈ': 176, 'ɑːˌ': 177, 'ɬˈ': 178, '”': 179, '“': 180, '“ˈ': 181, '“ˌ': 182, ';ˈ': 183, ';ˌ': 184, ':ˈ': 185, '1': 186, 'rˈ': 187, 'qˈ': 188, 'ᵻˌ': 189, 'ä': 190, '̞ˌ': 191, '̞': 192, 'ũˌ': 193, 'ʑˌ': 194, 'ᵝ': 195, 'ɽ': 196, 'ʲˌ': 197, 'ᵝˌ': 198, 'ũ': 199, 'ũˈ': 200, 'äˌ': 201, 'ɕ': 202, 'ɕˌ': 203, 'ɽˌ': 204, 'çˌ': 205, '…ˌ': 206, '̞ˈ': 207, 'äˈ': 208, 'ɽˈ': 209, 'ɸˌ': 210, 'ɴ': 211, 'ɸˈ': 212, 'ɕˈ': 213, 'ɸ': 214, 'ᵝˈ': 215, 'ʲˈ': 216, 'ĩ': 217, 'çˈ': 218, 'ĩˌ': 219, 'oˌ': 220, 'eˈ': 221, 'ʍ': 222, 'eˌ': 223, 'uˌ': 224, 'ʍˌ': 225, 'uˈ': 226, 'oˈ': 227, 'aˈ': 228}
|
||||
|
||||
def get_lang_symmap():
|
||||
symmap = {
|
||||
return {
|
||||
"en": 0,
|
||||
"ja": 1,
|
||||
}
|
||||
|
||||
def get_tone_symmap():
|
||||
return {
|
||||
"neutral": 0,
|
||||
}
|
||||
return symmap
|
||||
|
||||
def get_task_symmap():
|
||||
symmap = {
|
||||
return {
|
||||
"<tts>": 0,
|
||||
"<tts-c>": 1,
|
||||
"<ns>": 2,
|
||||
|
|
@ -54,7 +59,6 @@ def get_task_symmap():
|
|||
"<mask>": 6,
|
||||
"<eoe>": 7,
|
||||
}
|
||||
return symmap
|
||||
|
||||
def _replace_file_extension(path, suffix):
|
||||
return (path.parent / path.name.split(".")[0]).with_suffix(suffix)
|
||||
|
|
@ -237,6 +241,7 @@ class Dataset(_Dataset):
|
|||
self.spkr_symmap = self._get_spkr_symmap()
|
||||
self.spkr_group_symmap = self._get_spkr_group_symmap()
|
||||
self.lang_symmap = self._get_lang_symmap()
|
||||
self.tone_symmap = self._get_tone_symmap()
|
||||
self.task_symmap = self._get_task_symmap()
|
||||
|
||||
# assert len(self.phone_symmap) < 256, "Unique token count should be [0,255] to fit within uint8"
|
||||
|
|
@ -309,11 +314,14 @@ class Dataset(_Dataset):
|
|||
def _get_lang_symmap(self):
|
||||
return get_lang_symmap()
|
||||
|
||||
def _get_tone_symmap(self):
|
||||
return get_tone_symmap()
|
||||
|
||||
def _get_task_symmap(self):
|
||||
return get_task_symmap()
|
||||
|
||||
"""
|
||||
def get_task_token( self, token, levels=cfg.models.max_levels ):
|
||||
def get_task_token( self, token, levels=cfg.model.max_levels ):
|
||||
if not hasattr(self, "task_symmap"):
|
||||
self.task_symmap = self._get_task_symmap()
|
||||
return torch.Tensor([[ self.task_symmap[f'<{token}>'] for _ in range(levels) ]]).to(dtype=torch.int16)
|
||||
|
|
@ -339,7 +347,7 @@ class Dataset(_Dataset):
|
|||
choices = set(self.paths_by_spkr_name[spkr_name]) - {ignore}
|
||||
choices = [*choices]
|
||||
|
||||
# no other utterances, it'd make more sense to prune speakers with only one utterance in the validatoin step
|
||||
# no other utterances, it'd make more sense to prune speakers with only one utterance in the validation step
|
||||
if len(choices) == 0:
|
||||
choices = [*set(self.paths_by_spkr_name[spkr_name])]
|
||||
"""
|
||||
|
|
@ -622,8 +630,8 @@ class Dataset(_Dataset):
|
|||
"""
|
||||
|
||||
# trim to fit to requested prom/resps levels
|
||||
proms = proms[:, :cfg.models.prom_levels]
|
||||
resps = resps[:, :cfg.models.prom_levels]
|
||||
proms = proms[:, :cfg.model.prom_levels]
|
||||
resps = resps[:, :cfg.model.prom_levels]
|
||||
|
||||
|
||||
return dict(
|
||||
|
|
@ -928,7 +936,7 @@ if __name__ == "__main__":
|
|||
if task not in cfg.dataset.tasks_list:
|
||||
continue
|
||||
|
||||
print(text, task, cfg.models.prom_levels)
|
||||
print(text, task, cfg.model.prom_levels)
|
||||
print( proms.shape, resps.shape )
|
||||
|
||||
tokens = 0
|
||||
|
|
|
|||
|
|
@ -21,7 +21,7 @@ except Exception as e:
|
|||
cfg.inference.use_vocos = False
|
||||
|
||||
@cache
|
||||
def _load_encodec_model(device="cuda", levels=cfg.models.max_levels):
|
||||
def _load_encodec_model(device="cuda", levels=cfg.model.max_levels):
|
||||
# Instantiate a pretrained EnCodec model
|
||||
assert cfg.sample_rate == 24_000
|
||||
|
||||
|
|
@ -44,7 +44,7 @@ def _load_encodec_model(device="cuda", levels=cfg.models.max_levels):
|
|||
return model
|
||||
|
||||
@cache
|
||||
def _load_vocos_model(device="cuda", levels=cfg.models.max_levels):
|
||||
def _load_vocos_model(device="cuda", levels=cfg.model.max_levels):
|
||||
assert cfg.sample_rate == 24_000
|
||||
|
||||
model = Vocos.from_pretrained("charactr/vocos-encodec-24khz")
|
||||
|
|
@ -66,7 +66,7 @@ def _load_vocos_model(device="cuda", levels=cfg.models.max_levels):
|
|||
return model
|
||||
|
||||
@cache
|
||||
def _load_model(device="cuda", vocos=cfg.inference.use_vocos, levels=cfg.models.max_levels):
|
||||
def _load_model(device="cuda", vocos=cfg.inference.use_vocos, levels=cfg.model.max_levels):
|
||||
if vocos:
|
||||
model = _load_vocos_model(device, levels=levels)
|
||||
else:
|
||||
|
|
@ -80,7 +80,7 @@ def unload_model():
|
|||
|
||||
|
||||
@torch.inference_mode()
|
||||
def decode(codes: Tensor, device="cuda", levels=cfg.models.max_levels):
|
||||
def decode(codes: Tensor, device="cuda", levels=cfg.model.max_levels):
|
||||
"""
|
||||
Args:
|
||||
codes: (b q t)
|
||||
|
|
@ -117,7 +117,7 @@ def decode(codes: Tensor, device="cuda", levels=cfg.models.max_levels):
|
|||
return wav, model.sample_rate
|
||||
|
||||
# huh
|
||||
def decode_to_wave(resps: Tensor, device="cuda", levels=cfg.models.max_levels):
|
||||
def decode_to_wave(resps: Tensor, device="cuda", levels=cfg.model.max_levels):
|
||||
return decode(resps, device=device, levels=levels)
|
||||
|
||||
def decode_to_file(resps: Tensor, path: Path, device="cuda"):
|
||||
|
|
@ -131,7 +131,7 @@ def _replace_file_extension(path, suffix):
|
|||
|
||||
|
||||
@torch.inference_mode()
|
||||
def encode(wav: Tensor, sr: int = 24_000, device="cuda", levels=cfg.models.max_levels):
|
||||
def encode(wav: Tensor, sr: int = 24_000, device="cuda", levels=cfg.model.max_levels):
|
||||
"""
|
||||
Args:
|
||||
wav: (t)
|
||||
|
|
@ -224,7 +224,7 @@ def repeat_extend_audio( qnt, target ):
|
|||
|
||||
# merges two quantized audios together
|
||||
# I don't know if this works
|
||||
def merge_audio( *args, device="cpu", scale=[], levels=cfg.models.max_levels ):
|
||||
def merge_audio( *args, device="cpu", scale=[], levels=cfg.model.max_levels ):
|
||||
qnts = [*args]
|
||||
decoded = [ decode(qnt, device=device, levels=levels)[0] for qnt in qnts ]
|
||||
|
||||
|
|
|
|||
|
|
@ -26,7 +26,7 @@ from functools import cache
|
|||
|
||||
@cache
|
||||
def load_engines(training=True):
|
||||
models = get_models(cfg.models.get(), training=training)
|
||||
models = get_models(cfg.model.get(), training=training)
|
||||
engines = dict()
|
||||
|
||||
for name, model in models.items():
|
||||
|
|
@ -145,8 +145,8 @@ def load_engines(training=True):
|
|||
engine.freeze(freeze_all=False)
|
||||
|
||||
# copy embeddings if requested
|
||||
if cfg.models._embeddings is not None:
|
||||
embeddings_path = cfg.relpath / cfg.models._embeddings
|
||||
if cfg.model._embeddings is not None:
|
||||
embeddings_path = cfg.relpath / cfg.model._embeddings
|
||||
|
||||
if embeddings_path.exists():
|
||||
embeddings = torch.load(embeddings_path, map_location=torch.device(cfg.device))
|
||||
|
|
|
|||
|
|
@ -19,7 +19,7 @@ if deepspeed_available:
|
|||
import deepspeed
|
||||
|
||||
class TTS():
|
||||
def __init__( self, config=None, ar_ckpt=None, nar_ckpt=None, device=None, amp=None, dtype=None ):
|
||||
def __init__( self, config=None, model_ckpt=None, device=None, amp=None, dtype=None ):
|
||||
self.loading = True
|
||||
|
||||
self.input_sample_rate = 24000
|
||||
|
|
@ -53,7 +53,10 @@ class TTS():
|
|||
|
||||
self.symmap = None
|
||||
|
||||
def parse( name, model, state ):
|
||||
if model_ckpt:
|
||||
state = torch.load(model_ckpt)
|
||||
self.model = get_models(cfg.model.get(), training=False)[0]
|
||||
|
||||
if "userdata" in state and 'symmap' in state['userdata']:
|
||||
self.symmap = state['userdata']['symmap']
|
||||
elif "symmap" in state:
|
||||
|
|
@ -62,55 +65,26 @@ class TTS():
|
|||
if "module" in state:
|
||||
state = state['module']
|
||||
|
||||
model.load_state_dict(state)
|
||||
self.model.load_state_dict(state)
|
||||
|
||||
if cfg.inference.backend == "local" and deepspeed_available and cfg.trainer.deepspeed.inferencing:
|
||||
model = deepspeed.init_inference(model=model, mp_size=1, replace_with_kernel_inject=True, dtype=dtype if not amp else torch.float32).module
|
||||
|
||||
return model
|
||||
|
||||
if ar_ckpt and nar_ckpt:
|
||||
self.ar_ckpt = ar_ckpt
|
||||
self.nar_ckpt = nar_ckpt
|
||||
|
||||
models = get_models(cfg.models.get(), training=False)
|
||||
|
||||
for name, model in models.items():
|
||||
if name.startswith("ar"):
|
||||
state = torch.load(self.ar_ckpt)
|
||||
self.ar = parse( name, model, state )
|
||||
elif name.startswith("nar"):
|
||||
state = torch.load(self.nar_ckpt)
|
||||
self.nar = parse( name, model, state )
|
||||
|
||||
if name.startswith("ar+nar"):
|
||||
self.nar = self.ar
|
||||
self.model = deepspeed.init_inference(model=self.model, mp_size=1, replace_with_kernel_inject=True, dtype=dtype if not amp else torch.float32).module
|
||||
else:
|
||||
self.load_models()
|
||||
engines = load_engines(training=False)
|
||||
for name, engine in engines.items():
|
||||
self.model = engine.module
|
||||
break
|
||||
|
||||
if self.dtype != torch.int8:
|
||||
self.ar = self.ar.to(self.device, dtype=self.dtype if not self.amp else torch.float32)
|
||||
self.nar = self.nar.to(self.device, dtype=self.dtype if not self.amp else torch.float32)
|
||||
self.model = self.model.to(self.device, dtype=self.dtype if not self.amp else torch.float32)
|
||||
|
||||
self.ar.eval()
|
||||
self.nar.eval()
|
||||
self.model.eval()
|
||||
|
||||
if self.symmap is None:
|
||||
self.symmap = get_phone_symmap()
|
||||
|
||||
self.loading = False
|
||||
|
||||
def load_models( self ):
|
||||
engines = load_engines(training=False)
|
||||
for name, engine in engines.items():
|
||||
if name.startswith("ar"):
|
||||
self.ar = engine.module
|
||||
elif name.startswith("nar"):
|
||||
self.nar = engine.module
|
||||
|
||||
if name.startswith("ar+nar"):
|
||||
self.nar = self.ar
|
||||
|
||||
def encode_text( self, text, language="en" ):
|
||||
# already a tensor, return it
|
||||
if isinstance( text, Tensor ):
|
||||
|
|
@ -193,7 +167,7 @@ class TTS():
|
|||
lang = to_device(lang, self.device).to(torch.uint8)
|
||||
|
||||
with torch.autocast("cuda", dtype=self.dtype, enabled=self.amp):
|
||||
resps_list = self.ar(
|
||||
resps_list = self.model(
|
||||
text_list=[phns], proms_list=[prom], lang_list=[lang], max_steps=max_ar_steps, max_resp_context=max_ar_context,
|
||||
sampling_temperature=ar_temp,
|
||||
sampling_min_temperature=min_ar_temp,
|
||||
|
|
@ -205,7 +179,7 @@ class TTS():
|
|||
sampling_mirostat_eta=mirostat_eta,
|
||||
)
|
||||
resps_list = [r.unsqueeze(-1) for r in resps_list]
|
||||
resps_list = self.nar(
|
||||
resps_list = self.model(
|
||||
text_list=[phns], proms_list=[prom], lang_list=[lang], resps_list=resps_list,
|
||||
max_levels=max_nar_levels,
|
||||
sampling_temperature=nar_temp,
|
||||
|
|
|
|||
|
|
@ -1,19 +1,9 @@
|
|||
from .ar import AR
|
||||
from .nar import NAR
|
||||
from .ar_nar import AR_NAR
|
||||
|
||||
def get_model(cfg, training=True):
|
||||
if cfg.name == "ar":
|
||||
Model = AR
|
||||
elif cfg.name == "nar":
|
||||
Model = NAR
|
||||
elif cfg.name == "ar+nar":
|
||||
Model = AR_NAR
|
||||
else:
|
||||
raise f"invalid model name: {cfg.name}"
|
||||
name = cfg.name
|
||||
|
||||
model = Model(
|
||||
model = AR_NAR(
|
||||
n_tokens=cfg.tokens,
|
||||
d_model=cfg.dim,
|
||||
n_heads=cfg.heads,
|
||||
|
|
|
|||
|
|
@ -1,309 +0,0 @@
|
|||
from ..config import cfg
|
||||
from .base import Base, list_to_tensor, Categorical
|
||||
|
||||
import torch
|
||||
from torch.nn.utils.rnn import pad_sequence
|
||||
|
||||
from einops import rearrange
|
||||
from torch import Tensor
|
||||
from tqdm import trange
|
||||
|
||||
class AR(Base):
|
||||
@property
|
||||
def causal(self):
|
||||
return True
|
||||
|
||||
@property
|
||||
def norm_type(self):
|
||||
return "ln"
|
||||
|
||||
@property
|
||||
def arch_type(self) -> str:
|
||||
if hasattr(self, "config") and self.config:
|
||||
return self.config.arch_type
|
||||
return cfg.models.ar.arch_type
|
||||
|
||||
@property
|
||||
def n_prom_levels(self) -> int:
|
||||
return cfg.models.prom_levels
|
||||
|
||||
@property
|
||||
def n_resp_levels(self) -> int:
|
||||
if hasattr(self, "config") and self.config:
|
||||
return self.config.resp_levels
|
||||
return cfg.models.ar.resp_levels
|
||||
|
||||
@property
|
||||
def n_max_levels(self) -> int:
|
||||
return cfg.models.max_levels
|
||||
|
||||
@property
|
||||
def n_tasks(self) -> int:
|
||||
return cfg.models.ar.tasks
|
||||
|
||||
@property
|
||||
def n_langs(self) -> int:
|
||||
return cfg.models.ar.langs
|
||||
|
||||
@property
|
||||
def recurrent_chunk_size(self) -> int:
|
||||
if cfg.mode == "training":
|
||||
return 0
|
||||
return cfg.inference.recurrent_chunk_size
|
||||
|
||||
"""
|
||||
@property
|
||||
def rotary_embedding_base(self) -> float:
|
||||
if hasattr(self, "config") and self.config:
|
||||
return self.config.rotary_embedding_base
|
||||
return cfg.models.ar.rotary_embedding_base
|
||||
"""
|
||||
|
||||
@property
|
||||
def interleave(self) -> bool:
|
||||
if hasattr(self, "config") and self.config:
|
||||
return self.config.interleave
|
||||
return False
|
||||
|
||||
@property
|
||||
def monolithic(self) -> bool:
|
||||
return False
|
||||
|
||||
@property
|
||||
def version(self) -> int:
|
||||
if hasattr(self, "config") and self.config:
|
||||
return self.config.version
|
||||
return cfg.models.ar.version
|
||||
|
||||
def _prune(self, l: Tensor):
|
||||
indices = (l == self.stop_token).nonzero()
|
||||
if len(indices) == 0:
|
||||
return l
|
||||
return l[: indices.min().item()]
|
||||
|
||||
def _interleave( self, codes ):
|
||||
if not self.interleave:
|
||||
return codes
|
||||
|
||||
return codes.flatten()
|
||||
|
||||
def _deinterleave( self, codes, length = 0 ):
|
||||
if not self.interleave:
|
||||
return codes
|
||||
|
||||
return torch.unflatten( codes[:codes.shape[0] // self.n_prom_levels * self.n_prom_levels], 0, ( codes.shape[0] // self.n_prom_levels, self.n_prom_levels ) )
|
||||
|
||||
@staticmethod
|
||||
def _unsqueeze_list(x_list, axis=-1):
|
||||
return [x.unsqueeze(dim=axis) for x in x_list]
|
||||
|
||||
def forward(
|
||||
self,
|
||||
text_list: list[Tensor],
|
||||
proms_list: list[Tensor],
|
||||
resps_list: list[Tensor] | None = None,
|
||||
lang_list: list[Tensor] | None = None,
|
||||
max_steps: int = 1000,
|
||||
max_resp_context: int = -1,
|
||||
|
||||
sampling_temperature: float = 1.0,
|
||||
sampling_min_temperature: float = -1.0,
|
||||
sampling_top_k: int = -100,
|
||||
sampling_top_p: float = 1.0,
|
||||
sampling_repetition_penalty: float = 1.0,
|
||||
sampling_repetition_penalty_decay: float = 0.0,
|
||||
sampling_length_penalty: float = 0.0,
|
||||
sampling_beam_width: int = 0,
|
||||
|
||||
sampling_mirostat_tau: float = 0.0,
|
||||
sampling_mirostat_eta: float = 0.1,
|
||||
):
|
||||
if resps_list is not None:
|
||||
if self.interleave:
|
||||
resps_list = [self._interleave(r) for r in resps_list]
|
||||
else:
|
||||
resps_list = [r[..., 0] for r in resps_list] # guarantees we only have the first levels
|
||||
|
||||
return super().forward(
|
||||
text_list=text_list,
|
||||
proms_list=proms_list,
|
||||
resps_list=self._unsqueeze_list(resps_list),
|
||||
targ_list=resps_list,
|
||||
lang_list=lang_list,
|
||||
quant_levels=None,
|
||||
)
|
||||
|
||||
device = text_list[0].device
|
||||
batch_size = len(text_list)
|
||||
|
||||
sequence_list = [ torch.zeros(0, device=device).to(torch.int16) for _ in text_list ]
|
||||
stopped = torch.zeros(batch_size, device=device).bool()
|
||||
|
||||
recurrent_state = {} if cfg.inference.recurrent_forward else None
|
||||
mirostat = [
|
||||
{"n": 1024, "tau": sampling_mirostat_tau, "eta": sampling_mirostat_eta, "max_surprise": sampling_mirostat_eta * 2, "error_surprise": 0, "running_total_surprise": 0}
|
||||
] * batch_size if sampling_mirostat_tau > 0.0 else None
|
||||
|
||||
sampling_beam_width_use_logs = True
|
||||
scores = [ 1.0 ] * sampling_beam_width
|
||||
|
||||
if self.interleave:
|
||||
max_steps *= self.n_prom_levels
|
||||
|
||||
# get next in sequence
|
||||
for n in trange(max_steps // max(1, self.recurrent_chunk_size)):
|
||||
if max_resp_context > 0:
|
||||
resps_list = self._unsqueeze_list([ sequence[-max_resp_context:] for sequence in sequence_list ] )
|
||||
else:
|
||||
resps_list = self._unsqueeze_list(sequence_list)
|
||||
|
||||
logits = super().forward(
|
||||
text_list=text_list,
|
||||
proms_list=proms_list,
|
||||
resps_list=resps_list,
|
||||
|
||||
state=recurrent_state
|
||||
)
|
||||
|
||||
r = super().sample(
|
||||
logits=logits,
|
||||
resps_list=resps_list,
|
||||
|
||||
temperature=sampling_temperature,
|
||||
min_temperature=sampling_min_temperature,
|
||||
top_p=sampling_top_p,
|
||||
top_k=sampling_top_k,
|
||||
repetition_penalty=sampling_repetition_penalty,
|
||||
repetition_penalty_decay=sampling_repetition_penalty_decay,
|
||||
length_penalty=sampling_length_penalty,
|
||||
beam_width=sampling_beam_width,
|
||||
|
||||
mirostat=mirostat,
|
||||
)
|
||||
|
||||
if mirostat is not None:
|
||||
# r is the state
|
||||
mirostat = r
|
||||
# extract token from state
|
||||
r = [ state["token"] for state in mirostat ]
|
||||
# we do it here because the sampler will already expand our logits list
|
||||
elif sampling_beam_width > 0:
|
||||
# expand tuple
|
||||
r, s = r
|
||||
# first step, expand batch
|
||||
if batch_size == 1:
|
||||
batch_size *= sampling_beam_width
|
||||
text_list = text_list * sampling_beam_width
|
||||
proms_list = proms_list * sampling_beam_width
|
||||
sequence_list = sequence_list * sampling_beam_width
|
||||
stopped = torch.zeros(batch_size, device=device).bool()
|
||||
|
||||
# update scores
|
||||
if sampling_beam_width_use_logs:
|
||||
scores = [ (math.log(scores[i]) if scores[i] > 0 else 0) + math.log(score) for i, score in enumerate(s) ]
|
||||
else:
|
||||
scores = [ scores[i] * score for i, score in enumerate(s) ]
|
||||
|
||||
# append tokens
|
||||
for i, ri in enumerate(r):
|
||||
if self.stop_token in ri:
|
||||
stopped[i] = True
|
||||
sequence_list[i] = torch.cat([sequence_list[i], ri.to(device)])
|
||||
|
||||
# stop token found
|
||||
stopped |= r == self.stop_token
|
||||
if stopped.all().item():
|
||||
break
|
||||
|
||||
# pick the best scoring candidate
|
||||
# desu this is always going to be candidate 0
|
||||
if sampling_beam_width and len(scores) > 0:
|
||||
best_idx, best_score = (0, 0)
|
||||
for idx, score in enumerate(scores):
|
||||
if best_score > score:
|
||||
best_idx, best_score = idx, score
|
||||
|
||||
sequence_list = [sequence_list[best_idx]]
|
||||
|
||||
if self.interleave:
|
||||
sequence_list = [self._deinterleave(r) for r in sequence_list]
|
||||
return [self._prune(r) for r in sequence_list]
|
||||
|
||||
|
||||
def example_usage():
|
||||
cfg.trainer.backend = "local"
|
||||
from functools import partial
|
||||
|
||||
from einops import repeat
|
||||
|
||||
from ..emb.qnt import decode_to_file
|
||||
from ..engines import Engine
|
||||
from tqdm import tqdm
|
||||
from ..utils import wrapper as ml
|
||||
|
||||
device = "cuda"
|
||||
x8 = partial(repeat, pattern="t -> t l", l=cfg.models.prom_levels)
|
||||
symmap = {'<s>': 1, '</s>': 2, ' ': 3, '.': 4, ',': 5, '!': 6, '?': 7, 'p': 7, 'iː': 8, 'ɚ': 9, 'ˌ': 10, 'dˌ': 11, 'mˌ': 12, 'd': 13, 'ɹ': 14, 'tˈ': 15, 'pˌ': 16, 'uː': 17, 'l': 18, 'æ': 19, 'ɛ': 20, 'ɪ': 21, 'j': 22, 'ʊ': 23, 't': 24, 'n': 25, 'v': 26, 'a': 27, 'o': 28, 'ŋ': 29, 'w': 30, 'ʌ': 31, 'hˈ': 32, 'ɡˈ': 33, 'ə': 34, 'θˈ': 35, 'dˈ': 36, 'wˌ': 37, 'h': 38, 'z': 39, 'k': 40, 'ð': 41, 'ɡˌ': 42, 'ˈ': 43, 'fˈ': 44, 'i': 45, 's': 46, 'ʃ': 47, 'wˈ': 48, 'ðˈ': 49, 'ɹˈ': 50, 'lˈ': 51, 'ɡ': 52, 'oː': 53, 'mˈ': 54, 'e': 55, 'ɑː': 56, 'nˈ': 57, 'm': 58, 'θˌ': 59, 'sˈ': 60, 'f': 61, 'ɔː': 62, 'hˌ': 63, 'b': 64, 'jˈ': 65, 'ɐ': 66, 'ʒˈ': 67, 'θ': 68, 'bˈ': 69, 'ɾ': 70, 'ɜː': 71, 'ʌˈ': 72, 'ʃˌ': 73, 'bˌ': 74, 'kˈ': 75, 'ɔ': 76, 'zˈ': 77, 'ᵻ': 78, 'kˌ': 79, 'vˈ': 80, 'fˌ': 81, 'ʒ': 82, 'ʃˈ': 83, 'ɹˌ': 84, 'tˌ': 85, 'pˈ': 86, 'ðˌ': 87, 'sˌ': 88, 'nˌ': 89, 'lˌ': 90, '̩': 91, 'ʔ': 92, 'vˌ': 93, 'ɪˈ': 94, '"': 95, 'ɪˌ': 96, 'ʒˌ': 97, 'uːˌ': 98, 'ʊˈ': 99, 'jˌ': 100, 'uːˈ': 101, 'iːˈ': 102, 'zˌ': 103, '.ˈ': 104, '…': 105, 'ŋˌ': 106, 'ɐˌ': 107, '—ˈ': 108, 'iˌ': 109, 'iːˌ': 110, 'ɛː': 111, ')': 112, ')ˈ': 113, '(': 114, 'u': 115, '-': 116, 'ɖˈ': 117, 'iˈ': 118, 'ʰˈ': 119, 'ɟˈ': 120, '̃': 121, 'eː': 122, 'ɾˈ': 123, 'r': 124, 'ʰ': 125, '-ˌ': 126, 'ɫ': 127, 'q': 128, '—': 129, 'ʊˌ': 130, 'aː': 131, 'cˈ': 132, '…ˈ': 133, 'c': 134, 'ɳ': 135, 'ɐˈ': 136, 'x': 137, 'ʔˌ': 138, '.ˌ': 139, 'ɑ': 140, '?ˈ': 141, '̩ˈ': 142, '"ˈ': 143, ',ˈ': 144, 'ŋˈ': 145, 'əˌ': 146, '!ˈ': 147, '"ˌ': 148, '?ˌ': 149, ',ˌ': 150, '—ˌ': 151, '̩ˌ': 152, 'əˈ': 153, '!ˌ': 154, 'ɬ': 155, 'ʲ': 156, '¡': 157, 'ɯ': 158, 'qˌ': 159, 'ʑ': 160, 'ʑˈ': 161, '¿': 162, 'ɑːˈ': 163, 'iːː': 164, 'ɛˈ': 165, '¡ˈ': 166, 'æˈ': 167, 'ç': 168, 'ɾˌ': 169, 'ᵻˈ': 170, 'xˈ': 171, 'ɔːˈ': 172, ';': 173, 'ɬˌ': 174, ':': 175, 'ʔˈ': 176, 'ɑːˌ': 177, 'ɬˈ': 178}
|
||||
def tokenize(content, lang_marker="en"):
|
||||
split = content.split(" ")
|
||||
phones = [f"<s>"] + [ " " if not p else p for p in split ] + [f"</s>"]
|
||||
return torch.tensor([*map(symmap.get, phones)]).to()
|
||||
|
||||
qnt = torch.load("data/qnt.pt")[0].t()[:, :cfg.models.prom_levels].to(device)
|
||||
|
||||
text_list = [
|
||||
#torch.tensor([1, 2, 3], device=device),
|
||||
tokenize("ˈ a ɪ w ɪ l nˌ ɑː t ˈ æ s k ɐ sˈ ɛ k ə n d tˈ a ɪ m").to(device),
|
||||
]
|
||||
proms_list = [
|
||||
#x8(torch.tensor([1, 2, 3], device=device)),
|
||||
qnt.to(device),
|
||||
]
|
||||
resps_list = [
|
||||
qnt.to(device),
|
||||
]
|
||||
|
||||
text_list = text_list[:1]
|
||||
proms_list = proms_list[:1]
|
||||
resps_list = resps_list[:1]
|
||||
|
||||
kwargs = {
|
||||
'n_tokens': 1024,
|
||||
'd_model': 1024,
|
||||
'n_heads': 16,
|
||||
'n_layers': 24,
|
||||
}
|
||||
|
||||
"""
|
||||
try:
|
||||
kwargs['config'] = cfg.models.ar
|
||||
except Exception as e:
|
||||
pass
|
||||
"""
|
||||
|
||||
model = AR(**kwargs).to(device)
|
||||
steps = 500
|
||||
optimizer = ml.Prodigy(model.parameters(), lr=1.0)
|
||||
engine = Engine(model=model, optimizer=optimizer)
|
||||
|
||||
def sample( name, steps=600 ):
|
||||
engine.eval()
|
||||
out = engine(text_list, proms_list, max_steps=steps)
|
||||
for i, o in enumerate(out):
|
||||
wav, sr = decode_to_file(o, f"data/ar.{i}.{name}.wav", device=device)
|
||||
|
||||
def train():
|
||||
engine.train()
|
||||
t = trange(steps)
|
||||
for i in t:
|
||||
stats = {"step": i}
|
||||
stats |= engine.traverse(text_list=text_list, proms_list=proms_list, resps_list=resps_list)
|
||||
|
||||
tqdm.write(f"{stats}")
|
||||
|
||||
sample("init", 75)
|
||||
train()
|
||||
sample("final")
|
||||
|
||||
if __name__ == "__main__":
|
||||
example_usage()
|
||||
|
|
@ -25,29 +25,33 @@ class AR_NAR(Base):
|
|||
def arch_type(self) -> str:
|
||||
if hasattr(self, "config") and self.config:
|
||||
return self.config.arch_type
|
||||
return cfg.models.ar_nar.arch_type
|
||||
return cfg.model.arch_type
|
||||
|
||||
@property
|
||||
def n_prom_levels(self) -> int:
|
||||
return cfg.models.prom_levels
|
||||
return cfg.model.prom_levels
|
||||
|
||||
@property
|
||||
def n_resp_levels(self) -> int:
|
||||
if hasattr(self, "config") and self.config:
|
||||
return self.config.resp_levels
|
||||
return cfg.models.ar_nar.resp_levels
|
||||
return cfg.model.resp_levels
|
||||
|
||||
@property
|
||||
def n_max_levels(self) -> int:
|
||||
return cfg.models.max_levels
|
||||
return cfg.model.max_levels
|
||||
|
||||
@property
|
||||
def n_tasks(self) -> int:
|
||||
return cfg.models.ar_nar.tasks
|
||||
|
||||
return cfg.model.tasks
|
||||
|
||||
@property
|
||||
def n_langs(self) -> int:
|
||||
return cfg.models.ar_nar.langs
|
||||
return cfg.model.langs
|
||||
|
||||
@property
|
||||
def n_tones(self) -> int:
|
||||
return cfg.model.tones
|
||||
|
||||
@property
|
||||
def recurrent_chunk_size(self) -> int:
|
||||
|
|
@ -58,7 +62,7 @@ class AR_NAR(Base):
|
|||
def rotary_embedding_base(self) -> float:
|
||||
if hasattr(self, "config") and self.config:
|
||||
return self.config.rotary_embedding_base
|
||||
return cfg.models.ar_nar.rotary_embedding_base
|
||||
return cfg.model.rotary_embedding_base
|
||||
"""
|
||||
|
||||
@property
|
||||
|
|
@ -73,7 +77,7 @@ class AR_NAR(Base):
|
|||
def version(self) -> int:
|
||||
if hasattr(self, "config") and self.config:
|
||||
return self.config.version
|
||||
return cfg.models.ar_nar.version
|
||||
return cfg.model.version
|
||||
|
||||
def _prune(self, l: Tensor):
|
||||
indices = (l == self.stop_token).nonzero()
|
||||
|
|
@ -92,6 +96,7 @@ class AR_NAR(Base):
|
|||
resps_list: list[Tensor] | None = None,
|
||||
|
||||
lang_list: list[Tensor] | None = None,
|
||||
tone_list: list[Tensor] | None = None,
|
||||
|
||||
max_steps: int = 1000,
|
||||
max_levels: int = 0,
|
||||
|
|
@ -134,10 +139,10 @@ class AR_NAR(Base):
|
|||
else:
|
||||
quant_levels = torch.randint(0, self.n_resp_levels, (batch_size,)) # randomly select a target RVQ-bin level (0 being AR, 1+ being NAR)
|
||||
"""
|
||||
if cfg.models.ar_nar.p_ar_level == "auto" or cfg.models.ar_nar.p_ar_level is None:
|
||||
if cfg.model.p_ar_level == "auto" or cfg.model.p_ar_level is None:
|
||||
quant_levels = torch.randint(0, self.n_resp_levels, (batch_size,)) # randomly select a target RVQ-bin level (0 being AR, 1+ being NAR)
|
||||
else:
|
||||
quant_levels = torch.Tensor([ 0 if random.random() < cfg.models.ar_nar.p_ar_level else random.randint(1, self.n_resp_levels) for _ in range(batch_size) ])
|
||||
quant_levels = torch.Tensor([ 0 if random.random() < cfg.model.p_ar_level else random.randint(1, self.n_resp_levels) for _ in range(batch_size) ])
|
||||
"""
|
||||
|
||||
targ_list = [r[..., l] for r, l in zip(resps_list, quant_levels)] # ensures we only have 1 RVQ-bin (our target)
|
||||
|
|
@ -162,6 +167,7 @@ class AR_NAR(Base):
|
|||
resps_list=resps_list,
|
||||
targ_list=targ_list,
|
||||
lang_list=lang_list,
|
||||
tone_list=tone_list,
|
||||
quant_levels=quant_levels,
|
||||
)
|
||||
# is NAR
|
||||
|
|
@ -182,6 +188,7 @@ class AR_NAR(Base):
|
|||
proms_list=proms_list,
|
||||
resps_list=prev_list,
|
||||
lang_list=lang_list,
|
||||
tone_list=tone_list,
|
||||
quant_levels=quant_levels,
|
||||
)
|
||||
|
||||
|
|
@ -234,6 +241,7 @@ class AR_NAR(Base):
|
|||
proms_list=proms_list,
|
||||
resps_list=resps_list,
|
||||
lang_list=lang_list,
|
||||
tone_list=tone_list,
|
||||
state=recurrent_state
|
||||
)
|
||||
else:
|
||||
|
|
@ -242,6 +250,7 @@ class AR_NAR(Base):
|
|||
proms_list=proms_list,
|
||||
resps_list=resps_list,
|
||||
lang_list=lang_list,
|
||||
tone_list=tone_list,
|
||||
state=recurrent_state
|
||||
)
|
||||
|
||||
|
|
@ -312,14 +321,14 @@ def example_usage():
|
|||
import re
|
||||
|
||||
device = "cuda"
|
||||
x8 = partial(repeat, pattern="t -> t l", l=cfg.models.prom_levels)
|
||||
x8 = partial(repeat, pattern="t -> t l", l=cfg.model.prom_levels)
|
||||
symmap = {'<s>': 1, '</s>': 2, ' ': 3, '.': 4, ',': 5, '!': 6, '?': 7, 'p': 7, 'iː': 8, 'ɚ': 9, 'ˌ': 10, 'dˌ': 11, 'mˌ': 12, 'd': 13, 'ɹ': 14, 'tˈ': 15, 'pˌ': 16, 'uː': 17, 'l': 18, 'æ': 19, 'ɛ': 20, 'ɪ': 21, 'j': 22, 'ʊ': 23, 't': 24, 'n': 25, 'v': 26, 'a': 27, 'o': 28, 'ŋ': 29, 'w': 30, 'ʌ': 31, 'hˈ': 32, 'ɡˈ': 33, 'ə': 34, 'θˈ': 35, 'dˈ': 36, 'wˌ': 37, 'h': 38, 'z': 39, 'k': 40, 'ð': 41, 'ɡˌ': 42, 'ˈ': 43, 'fˈ': 44, 'i': 45, 's': 46, 'ʃ': 47, 'wˈ': 48, 'ðˈ': 49, 'ɹˈ': 50, 'lˈ': 51, 'ɡ': 52, 'oː': 53, 'mˈ': 54, 'e': 55, 'ɑː': 56, 'nˈ': 57, 'm': 58, 'θˌ': 59, 'sˈ': 60, 'f': 61, 'ɔː': 62, 'hˌ': 63, 'b': 64, 'jˈ': 65, 'ɐ': 66, 'ʒˈ': 67, 'θ': 68, 'bˈ': 69, 'ɾ': 70, 'ɜː': 71, 'ʌˈ': 72, 'ʃˌ': 73, 'bˌ': 74, 'kˈ': 75, 'ɔ': 76, 'zˈ': 77, 'ᵻ': 78, 'kˌ': 79, 'vˈ': 80, 'fˌ': 81, 'ʒ': 82, 'ʃˈ': 83, 'ɹˌ': 84, 'tˌ': 85, 'pˈ': 86, 'ðˌ': 87, 'sˌ': 88, 'nˌ': 89, 'lˌ': 90, '̩': 91, 'ʔ': 92, 'vˌ': 93, 'ɪˈ': 94, '"': 95, 'ɪˌ': 96, 'ʒˌ': 97, 'uːˌ': 98, 'ʊˈ': 99, 'jˌ': 100, 'uːˈ': 101, 'iːˈ': 102, 'zˌ': 103, '.ˈ': 104, '…': 105, 'ŋˌ': 106, 'ɐˌ': 107, '—ˈ': 108, 'iˌ': 109, 'iːˌ': 110, 'ɛː': 111, ')': 112, ')ˈ': 113, '(': 114, 'u': 115, '-': 116, 'ɖˈ': 117, 'iˈ': 118, 'ʰˈ': 119, 'ɟˈ': 120, '̃': 121, 'eː': 122, 'ɾˈ': 123, 'r': 124, 'ʰ': 125, '-ˌ': 126, 'ɫ': 127, 'q': 128, '—': 129, 'ʊˌ': 130, 'aː': 131, 'cˈ': 132, '…ˈ': 133, 'c': 134, 'ɳ': 135, 'ɐˈ': 136, 'x': 137, 'ʔˌ': 138, '.ˌ': 139, 'ɑ': 140, '?ˈ': 141, '̩ˈ': 142, '"ˈ': 143, ',ˈ': 144, 'ŋˈ': 145, 'əˌ': 146, '!ˈ': 147, '"ˌ': 148, '?ˌ': 149, ',ˌ': 150, '—ˌ': 151, '̩ˌ': 152, 'əˈ': 153, '!ˌ': 154, 'ɬ': 155, 'ʲ': 156, '¡': 157, 'ɯ': 158, 'qˌ': 159, 'ʑ': 160, 'ʑˈ': 161, '¿': 162, 'ɑːˈ': 163, 'iːː': 164, 'ɛˈ': 165, '¡ˈ': 166, 'æˈ': 167, 'ç': 168, 'ɾˌ': 169, 'ᵻˈ': 170, 'xˈ': 171, 'ɔːˈ': 172, ';': 173, 'ɬˌ': 174, ':': 175, 'ʔˈ': 176, 'ɑːˌ': 177, 'ɬˈ': 178}
|
||||
def tokenize(content, lang_marker="en"):
|
||||
split = content.split(" ")
|
||||
phones = [f"<s>"] + [ " " if not p else p for p in split ] + [f"</s>"]
|
||||
return torch.tensor([*map(symmap.get, phones)])
|
||||
|
||||
qnt = torch.load("data/qnt.pt")[0].t()[:, :cfg.models.prom_levels].to(device)
|
||||
qnt = torch.load("data/qnt.pt")[0].t()[:, :cfg.model.prom_levels].to(device)
|
||||
|
||||
cfg.hyperparameters.gradient_accumulation_steps = 1
|
||||
|
||||
|
|
@ -359,7 +368,7 @@ def example_usage():
|
|||
|
||||
"""
|
||||
try:
|
||||
kwargs['config'] = cfg.models.ar_nar
|
||||
kwargs['config'] = cfg.model
|
||||
except Exception as e:
|
||||
pass
|
||||
"""
|
||||
|
|
@ -374,8 +383,8 @@ def example_usage():
|
|||
|
||||
# copy embeddings if requested
|
||||
"""
|
||||
if cfg.models._embeddings is not None:
|
||||
embeddings_path = cfg.relpath / cfg.models._embeddings
|
||||
if cfg.model._embeddings is not None:
|
||||
embeddings_path = cfg.relpath / cfg.model._embeddings
|
||||
|
||||
if embeddings_path.exists():
|
||||
embeddings = torch.load(embeddings_path, map_location=torch.device(cfg.device))
|
||||
|
|
|
|||
|
|
@ -262,11 +262,15 @@ class Base(nn.Module):
|
|||
@property
|
||||
def n_langs(self) -> int:
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
@property
|
||||
def n_tasks(self) -> int:
|
||||
raise NotImplementedError
|
||||
|
||||
@property
|
||||
def n_tones(self) -> int:
|
||||
raise NotImplementedError
|
||||
|
||||
@property
|
||||
def recurrent_chunk_size(self) -> int:
|
||||
raise NotImplementedError
|
||||
|
|
@ -343,6 +347,7 @@ class Base(nn.Module):
|
|||
|
||||
self.text_emb = Embedding(n_tokens, d_model)
|
||||
self.langs_emb = None
|
||||
self.tones_emb = None
|
||||
self.tasks_emb = None
|
||||
|
||||
if self.version == 1: # legacy
|
||||
|
|
@ -359,6 +364,9 @@ class Base(nn.Module):
|
|||
if self.version >= 3:
|
||||
self.langs_emb = Embedding(self.n_langs, d_model) if self.n_langs > 0 else None
|
||||
self.tasks_emb = Embedding(self.n_tasks, d_model) if self.n_tasks > 0 else None
|
||||
|
||||
if self.version >= 4:
|
||||
self.tones_emb = Embedding(self.n_tones, d_model) if self.n_tones > 0 else None
|
||||
|
||||
self.sep = nn.Parameter(torch.randn(d_model))
|
||||
|
||||
|
|
@ -522,53 +530,15 @@ class Base(nn.Module):
|
|||
ignore_index=self.ignore_index,
|
||||
)
|
||||
|
||||
def forward(
|
||||
def _forward(
|
||||
self,
|
||||
text_list: list[Tensor],
|
||||
proms_list: list[Tensor],
|
||||
resps_list: list[Tensor],
|
||||
targ_list: list[Tensor] | None = None,
|
||||
|
||||
lang_list: list[Tensor] | None = None,
|
||||
|
||||
quant_levels: Tensor | None = None,
|
||||
state: dict | list | None = None,
|
||||
inputs,
|
||||
mask = None,
|
||||
state = None,
|
||||
):
|
||||
batch_size = len(text_list)
|
||||
|
||||
if self.langs_emb is None:
|
||||
lang_list = None
|
||||
|
||||
x_list = self._samplewise_merge_tensors(
|
||||
self.text_emb(text_list),
|
||||
self.langs_emb(lang_list) if lang_list is not None else None,
|
||||
self.proms_emb(proms_list),
|
||||
self.resps_emb(resps_list, quant_levels),
|
||||
sep=self.sep,
|
||||
)
|
||||
|
||||
|
||||
x, m = list_to_tensor(x_list)
|
||||
x = inputs
|
||||
m = mask.squeeze(-1).int()
|
||||
aux_loss = None
|
||||
|
||||
device = x.device
|
||||
|
||||
# pad our input and mask, but retain the original length by doing it after
|
||||
if self.l_padding and x.shape[1] % self.l_padding != 0:
|
||||
# pad input
|
||||
shape = list(x.shape)
|
||||
shape[1] = self.l_padding - shape[1] % self.l_padding
|
||||
|
||||
padding = torch.zeros(shape, dtype=x.dtype, device=x.device)
|
||||
x = torch.cat([x, padding], dim=1)
|
||||
|
||||
# pad mask
|
||||
shape[2] = 1
|
||||
padding = torch.zeros(shape, dtype=x.dtype, device=x.device)
|
||||
m = torch.cat([m, padding], dim=1)
|
||||
|
||||
# for simplicity
|
||||
mask = m.squeeze(-1).int()
|
||||
|
||||
"""
|
||||
# Broken
|
||||
|
|
@ -587,7 +557,7 @@ class Base(nn.Module):
|
|||
xi = x[:, n, :].unsqueeze(1)
|
||||
|
||||
kwargs = dict(
|
||||
attention_mask=mask,
|
||||
attention_mask=m,
|
||||
inputs_embeds=xi,
|
||||
past_key_values=state,
|
||||
use_cache=True,
|
||||
|
|
@ -603,9 +573,9 @@ class Base(nn.Module):
|
|||
"""
|
||||
|
||||
# HF transformer derived model
|
||||
if self.arch_type == "llama" or self.arch_type == "mistral" or self.arch_type == "mixtral":
|
||||
if self.arch_type in ["llama", "mistral", "mixtral"]:
|
||||
kwargs = dict(
|
||||
attention_mask=mask,
|
||||
attention_mask=m,
|
||||
inputs_embeds=x,
|
||||
past_key_values=state,
|
||||
use_cache=True,
|
||||
|
|
@ -632,7 +602,7 @@ class Base(nn.Module):
|
|||
x = self.sin_emb.add_pe(x)
|
||||
# pass our inputs through the transformer
|
||||
for block in self.blocks:
|
||||
x = block(x, mask, l)
|
||||
x = block(x, m, l)
|
||||
elif self.arch_type == "retnet":
|
||||
# pass our inputs through the RetNet
|
||||
x, _ = self.model(x, incremental_state=state, token_embeddings=x, features_only=True)
|
||||
|
|
@ -642,7 +612,7 @@ class Base(nn.Module):
|
|||
first = state is None or len(state) == 0
|
||||
|
||||
kwargs = dict(
|
||||
attention_mask=mask,
|
||||
attention_mask=m,
|
||||
inputs_embeds=x if first else x[:, -1, :].unsqueeze(1),
|
||||
past_key_values=None if first else state,
|
||||
use_cache=True,
|
||||
|
|
@ -659,8 +629,76 @@ class Base(nn.Module):
|
|||
x = self.model(x)
|
||||
|
||||
# output projection layer with masking
|
||||
x = self.classifier(x) * mask
|
||||
|
||||
x = self.classifier(x) * m
|
||||
return x, state, aux_loss
|
||||
|
||||
def forward(
|
||||
self,
|
||||
text_list: list[Tensor],
|
||||
proms_list: list[Tensor],
|
||||
resps_list: list[Tensor],
|
||||
targ_list: list[Tensor] | None = None,
|
||||
|
||||
lang_list: list[Tensor] | None = None,
|
||||
tone_list: list[Tensor] | None = None,
|
||||
|
||||
quant_levels: Tensor | None = None,
|
||||
state: dict | list | None = None,
|
||||
):
|
||||
device = text_list[0].device
|
||||
batch_size = len(text_list)
|
||||
|
||||
# silently ignore languages if model does not have it
|
||||
if self.langs_emb is None:
|
||||
lang_list = None
|
||||
# inject default language
|
||||
elif lang_list is None:
|
||||
lang_list = [ torch.Tensor([ 0 ]).to(dtype=torch.uint8, device=device) for _ in range(batch_size) ]
|
||||
|
||||
# silently ignore tones if model does not have it
|
||||
if self.tones_emb is None:
|
||||
tone_list = None
|
||||
# inject default tone
|
||||
elif tone_list is None:
|
||||
tone_list = [ torch.Tensor([ 0 ]).to(dtype=torch.uint8, device=device) for _ in range(batch_size) ]
|
||||
|
||||
"""
|
||||
# Typical sequence format
|
||||
# To-do: integrate tasks again
|
||||
<s><text></s><sep><lang><sep><prom><sep><tone><sep><resp><stop>
|
||||
"""
|
||||
x_list = self._samplewise_merge_tensors(
|
||||
self.text_emb(text_list),
|
||||
self.langs_emb(lang_list) if lang_list is not None else None,
|
||||
self.proms_emb(proms_list),
|
||||
self.tones_emb(tone_list) if tone_list is not None else None,
|
||||
self.resps_emb(resps_list, quant_levels),
|
||||
sep=self.sep,
|
||||
)
|
||||
|
||||
x, m = list_to_tensor(x_list)
|
||||
|
||||
# pad our input and mask, but retain the original length by doing it after
|
||||
if self.l_padding and x.shape[1] % self.l_padding != 0:
|
||||
# pad input
|
||||
shape = list(x.shape)
|
||||
shape[1] = self.l_padding - shape[1] % self.l_padding
|
||||
|
||||
padding = torch.zeros(shape, dtype=x.dtype, device=x.device)
|
||||
x = torch.cat([x, padding], dim=1)
|
||||
|
||||
# pad mask
|
||||
shape[2] = 1
|
||||
padding = torch.zeros(shape, dtype=x.dtype, device=x.device)
|
||||
m = torch.cat([m, padding], dim=1)
|
||||
|
||||
|
||||
x, state, aux_loss = self._forward(
|
||||
inputs=x,
|
||||
mask=m,
|
||||
state=state,
|
||||
)
|
||||
|
||||
# Remove padding
|
||||
logits = [ hi[:li] for hi, li in zip(x, map(len, x_list)) ]
|
||||
|
|
@ -790,7 +828,7 @@ def example_usage():
|
|||
from .nar import NAR
|
||||
|
||||
device = "cuda"
|
||||
x8 = partial(repeat, pattern="t -> t l", l=cfg.models.prom_levels)
|
||||
x8 = partial(repeat, pattern="t -> t l", l=cfg.model.prom_levels)
|
||||
symmap = {'<s>': 1, '</s>': 2, ' ': 3, '.': 4, ',': 5, '!': 6, '?': 7, 'p': 7, 'iː': 8, 'ɚ': 9, 'ˌ': 10, 'dˌ': 11, 'mˌ': 12, 'd': 13, 'ɹ': 14, 'tˈ': 15, 'pˌ': 16, 'uː': 17, 'l': 18, 'æ': 19, 'ɛ': 20, 'ɪ': 21, 'j': 22, 'ʊ': 23, 't': 24, 'n': 25, 'v': 26, 'a': 27, 'o': 28, 'ŋ': 29, 'w': 30, 'ʌ': 31, 'hˈ': 32, 'ɡˈ': 33, 'ə': 34, 'θˈ': 35, 'dˈ': 36, 'wˌ': 37, 'h': 38, 'z': 39, 'k': 40, 'ð': 41, 'ɡˌ': 42, 'ˈ': 43, 'fˈ': 44, 'i': 45, 's': 46, 'ʃ': 47, 'wˈ': 48, 'ðˈ': 49, 'ɹˈ': 50, 'lˈ': 51, 'ɡ': 52, 'oː': 53, 'mˈ': 54, 'e': 55, 'ɑː': 56, 'nˈ': 57, 'm': 58, 'θˌ': 59, 'sˈ': 60, 'f': 61, 'ɔː': 62, 'hˌ': 63, 'b': 64, 'jˈ': 65, 'ɐ': 66, 'ʒˈ': 67, 'θ': 68, 'bˈ': 69, 'ɾ': 70, 'ɜː': 71, 'ʌˈ': 72, 'ʃˌ': 73, 'bˌ': 74, 'kˈ': 75, 'ɔ': 76, 'zˈ': 77, 'ᵻ': 78, 'kˌ': 79, 'vˈ': 80, 'fˌ': 81, 'ʒ': 82, 'ʃˈ': 83, 'ɹˌ': 84, 'tˌ': 85, 'pˈ': 86, 'ðˌ': 87, 'sˌ': 88, 'nˌ': 89, 'lˌ': 90, '̩': 91, 'ʔ': 92, 'vˌ': 93, 'ɪˈ': 94, '"': 95, 'ɪˌ': 96, 'ʒˌ': 97, 'uːˌ': 98, 'ʊˈ': 99, 'jˌ': 100, 'uːˈ': 101, 'iːˈ': 102, 'zˌ': 103, '.ˈ': 104, '…': 105, 'ŋˌ': 106, 'ɐˌ': 107, '—ˈ': 108, 'iˌ': 109, 'iːˌ': 110, 'ɛː': 111, ')': 112, ')ˈ': 113, '(': 114, 'u': 115, '-': 116, 'ɖˈ': 117, 'iˈ': 118, 'ʰˈ': 119, 'ɟˈ': 120, '̃': 121, 'eː': 122, 'ɾˈ': 123, 'r': 124, 'ʰ': 125, '-ˌ': 126, 'ɫ': 127, 'q': 128, '—': 129, 'ʊˌ': 130, 'aː': 131, 'cˈ': 132, '…ˈ': 133, 'c': 134, 'ɳ': 135, 'ɐˈ': 136, 'x': 137, 'ʔˌ': 138, '.ˌ': 139, 'ɑ': 140, '?ˈ': 141, '̩ˈ': 142, '"ˈ': 143, ',ˈ': 144, 'ŋˈ': 145, 'əˌ': 146, '!ˈ': 147, '"ˌ': 148, '?ˌ': 149, ',ˌ': 150, '—ˌ': 151, '̩ˌ': 152, 'əˈ': 153, '!ˌ': 154, 'ɬ': 155, 'ʲ': 156, '¡': 157, 'ɯ': 158, 'qˌ': 159, 'ʑ': 160, 'ʑˈ': 161, '¿': 162, 'ɑːˈ': 163, 'iːː': 164, 'ɛˈ': 165, '¡ˈ': 166, 'æˈ': 167, 'ç': 168, 'ɾˌ': 169, 'ᵻˈ': 170, 'xˈ': 171, 'ɔːˈ': 172, ';': 173, 'ɬˌ': 174, ':': 175, 'ʔˈ': 176, 'ɑːˌ': 177, 'ɬˈ': 178}
|
||||
def tokenize(content, lang_marker="en"):
|
||||
split = content.split(" ")
|
||||
|
|
@ -812,7 +850,7 @@ def example_usage():
|
|||
|
||||
train = True
|
||||
|
||||
qnt = torch.load("data/qnt.pt")[0].t()[:, :cfg.models.prom_levels].to(device)
|
||||
qnt = torch.load("data/qnt.pt")[0].t()[:, :cfg.model.prom_levels].to(device)
|
||||
text_list = [
|
||||
tokenize("ˈ a ɪ w ɪ l nˌ ɑː t ˈ æ s k ɐ sˈ ɛ k ə n d tˈ a ɪ m").to(device),
|
||||
#tokenize("ˌ ɔ n ɡˌ o ʊ ɪ ŋ hˈ o ʊ m ð ə tˈ uː f ɹˈ ɛ n d z fˈ a ʊ n d ɐ lˈ ɛ ɾ ɚ f ɹ ʌ m ˈ æ θ o ʊ z , hˌ uː d ɪ zˈ a ɪ ɚ d ðˌ ɛ m t ə mˈ iː t hˌ ɪ m æ t ð ə ɡ ɹˈ æ n d t ʃˈ ɑː ɹ l ɪ mˌ æ ɡ n i ɔ n ð ə fˈ ɑː l o ʊ ɪ ŋ dˈ e ɪ .").to(device),
|
||||
|
|
|
|||
|
|
@ -1,235 +0,0 @@
|
|||
from ..config import cfg
|
||||
from .base import Base
|
||||
|
||||
import torch
|
||||
|
||||
from torch import Tensor
|
||||
from tqdm import trange
|
||||
|
||||
class NAR(Base):
|
||||
@property
|
||||
def causal(self):
|
||||
return False
|
||||
|
||||
@property
|
||||
def arch_type(self) -> str:
|
||||
if hasattr(self, "config") and self.config:
|
||||
return self.config.arch_type
|
||||
return cfg.models.nar.arch_type
|
||||
|
||||
@property
|
||||
def norm_type(self):
|
||||
return "ln" if self.n_resp_levels == 1 else "adaln"
|
||||
|
||||
@property
|
||||
def n_prom_levels(self) -> int:
|
||||
return cfg.models.prom_levels
|
||||
|
||||
@property
|
||||
def n_resp_levels(self) -> int:
|
||||
if hasattr(self, "config") and self.config:
|
||||
return self.config.resp_levels
|
||||
return cfg.models.nar.resp_levels
|
||||
|
||||
@property
|
||||
def n_max_levels(self) -> int:
|
||||
return cfg.models.max_levels
|
||||
|
||||
@property
|
||||
def n_tasks(self) -> int:
|
||||
return cfg.models.nar.tasks
|
||||
|
||||
@property
|
||||
def n_langs(self) -> int:
|
||||
return cfg.models.nar.langs
|
||||
|
||||
@property
|
||||
def version(self) -> int:
|
||||
if hasattr(self, "config") and self.config:
|
||||
return self.config.version
|
||||
return cfg.models.nar.version
|
||||
|
||||
@property
|
||||
def recurrent_chunk_size(self) -> int:
|
||||
return 0
|
||||
|
||||
"""
|
||||
@property
|
||||
def rotary_embedding_base(self) -> float:
|
||||
if hasattr(self, "config") and self.config:
|
||||
return self.config.rotary_embedding_base
|
||||
return cfg.models.nar.rotary_embedding_base
|
||||
"""
|
||||
|
||||
@property
|
||||
def interleave(self) -> bool:
|
||||
return False
|
||||
|
||||
@property
|
||||
def monolithic(self) -> bool:
|
||||
return False
|
||||
|
||||
def forward(
|
||||
self,
|
||||
text_list: list[Tensor],
|
||||
proms_list: list[Tensor],
|
||||
resps_list: list[Tensor],
|
||||
lang_list: list[Tensor] | None = None,
|
||||
max_levels: int = 0,
|
||||
sampling_temperature: float = 0.2,
|
||||
sampling_min_temperature: float = -1.0,
|
||||
sampling_top_k: int = -100,
|
||||
sampling_top_p: float = 1.0,
|
||||
sampling_repetition_penalty: float = 1.0,
|
||||
sampling_repetition_penalty_decay: float = 0.0,
|
||||
sampling_length_penalty: float = 0.0, # unused
|
||||
sampling_beam_width: int = 0, # unused
|
||||
sampling_mirostat_tau: float = 0.0, # unused
|
||||
):
|
||||
"""
|
||||
Args:
|
||||
text_list: [t] * b
|
||||
proms_list: [t' l] * b, l=8
|
||||
resps_list: [t'' l] * b, l=1 or 8, 1 for testing and 8 for training.
|
||||
Returns:
|
||||
[t'' l], l=8 if testing. empty list will be returned during training.
|
||||
"""
|
||||
|
||||
n_levels_set = {r.shape[-1] for r in resps_list}
|
||||
|
||||
if len(n_levels_set) > 1:
|
||||
raise ValueError(f"Please give only one level, got {n_levels_set}.")
|
||||
|
||||
n_levels = next(iter(n_levels_set))
|
||||
|
||||
device = text_list[0].device
|
||||
|
||||
if n_levels == self.n_resp_levels + 1:
|
||||
assert resps_list is not None
|
||||
|
||||
quant_levels = torch.randint(0, self.n_resp_levels, (len(resps_list),))
|
||||
|
||||
prev_list = [o[..., : l + 1] for o, l in zip(resps_list, quant_levels)]
|
||||
targ_list = [o[..., l + 1] for o, l in zip(resps_list, quant_levels)]
|
||||
|
||||
#quant_levels = quant_levels.to(device=device)
|
||||
|
||||
logits = super().forward(
|
||||
text_list=text_list,
|
||||
proms_list=proms_list,
|
||||
resps_list=prev_list,
|
||||
targ_list=targ_list,
|
||||
lang_list=lang_list,
|
||||
quant_levels=quant_levels,
|
||||
)
|
||||
|
||||
prev_list = []
|
||||
else:
|
||||
prev_list = resps_list
|
||||
if max_levels == 0:
|
||||
max_levels = self.n_resp_levels
|
||||
|
||||
while True:
|
||||
level = prev_list[0].shape[-1] - 1
|
||||
|
||||
if level >= max_levels: # min(max_levels, self.n_resp_levels): # commented out to experiment with exceeding trained levels
|
||||
break
|
||||
|
||||
quant_levels = torch.full((len(text_list),), level, device=device)
|
||||
|
||||
logits = super().forward(
|
||||
text_list=text_list,
|
||||
proms_list=proms_list,
|
||||
resps_list=prev_list,
|
||||
lang_list=lang_list,
|
||||
quant_levels=quant_levels,
|
||||
)
|
||||
|
||||
resps_list = super().sample(
|
||||
logits=logits,
|
||||
resps_list=prev_list,
|
||||
quant_levels=quant_levels,
|
||||
|
||||
temperature=sampling_temperature,
|
||||
min_temperature=sampling_min_temperature,
|
||||
top_p=sampling_top_p,
|
||||
top_k=sampling_top_k,
|
||||
repetition_penalty=sampling_repetition_penalty,
|
||||
repetition_penalty_decay=sampling_repetition_penalty_decay,
|
||||
#length_penalty=sampling_length_penalty,
|
||||
#beam_width=sampling_beam_width,
|
||||
#mirostat_tau=sampling_mirostat_tau,
|
||||
#mirostat_state=mirostat_state,
|
||||
)
|
||||
|
||||
prev_list = [ torch.cat([rs, r.unsqueeze(-1).to(device)], dim=-1) for rs, r in zip(prev_list, resps_list) ]
|
||||
|
||||
return prev_list
|
||||
|
||||
def example_usage():
|
||||
cfg.trainer.backend = "local"
|
||||
from functools import partial
|
||||
|
||||
from einops import repeat
|
||||
|
||||
from ..emb.qnt import decode_to_file
|
||||
from ..engines import Engine
|
||||
from tqdm import tqdm
|
||||
from ..utils import wrapper as ml
|
||||
|
||||
device = "cuda"
|
||||
x8 = partial(repeat, pattern="t -> t l", l=cfg.models.prom_levels)
|
||||
symmap = {'<s>': 1, '</s>': 2, ' ': 3, '.': 4, ',': 5, '!': 6, '?': 7, 'p': 7, 'iː': 8, 'ɚ': 9, 'ˌ': 10, 'dˌ': 11, 'mˌ': 12, 'd': 13, 'ɹ': 14, 'tˈ': 15, 'pˌ': 16, 'uː': 17, 'l': 18, 'æ': 19, 'ɛ': 20, 'ɪ': 21, 'j': 22, 'ʊ': 23, 't': 24, 'n': 25, 'v': 26, 'a': 27, 'o': 28, 'ŋ': 29, 'w': 30, 'ʌ': 31, 'hˈ': 32, 'ɡˈ': 33, 'ə': 34, 'θˈ': 35, 'dˈ': 36, 'wˌ': 37, 'h': 38, 'z': 39, 'k': 40, 'ð': 41, 'ɡˌ': 42, 'ˈ': 43, 'fˈ': 44, 'i': 45, 's': 46, 'ʃ': 47, 'wˈ': 48, 'ðˈ': 49, 'ɹˈ': 50, 'lˈ': 51, 'ɡ': 52, 'oː': 53, 'mˈ': 54, 'e': 55, 'ɑː': 56, 'nˈ': 57, 'm': 58, 'θˌ': 59, 'sˈ': 60, 'f': 61, 'ɔː': 62, 'hˌ': 63, 'b': 64, 'jˈ': 65, 'ɐ': 66, 'ʒˈ': 67, 'θ': 68, 'bˈ': 69, 'ɾ': 70, 'ɜː': 71, 'ʌˈ': 72, 'ʃˌ': 73, 'bˌ': 74, 'kˈ': 75, 'ɔ': 76, 'zˈ': 77, 'ᵻ': 78, 'kˌ': 79, 'vˈ': 80, 'fˌ': 81, 'ʒ': 82, 'ʃˈ': 83, 'ɹˌ': 84, 'tˌ': 85, 'pˈ': 86, 'ðˌ': 87, 'sˌ': 88, 'nˌ': 89, 'lˌ': 90, '̩': 91, 'ʔ': 92, 'vˌ': 93, 'ɪˈ': 94, '"': 95, 'ɪˌ': 96, 'ʒˌ': 97, 'uːˌ': 98, 'ʊˈ': 99, 'jˌ': 100, 'uːˈ': 101, 'iːˈ': 102, 'zˌ': 103, '.ˈ': 104, '…': 105, 'ŋˌ': 106, 'ɐˌ': 107, '—ˈ': 108, 'iˌ': 109, 'iːˌ': 110, 'ɛː': 111, ')': 112, ')ˈ': 113, '(': 114, 'u': 115, '-': 116, 'ɖˈ': 117, 'iˈ': 118, 'ʰˈ': 119, 'ɟˈ': 120, '̃': 121, 'eː': 122, 'ɾˈ': 123, 'r': 124, 'ʰ': 125, '-ˌ': 126, 'ɫ': 127, 'q': 128, '—': 129, 'ʊˌ': 130, 'aː': 131, 'cˈ': 132, '…ˈ': 133, 'c': 134, 'ɳ': 135, 'ɐˈ': 136, 'x': 137, 'ʔˌ': 138, '.ˌ': 139, 'ɑ': 140, '?ˈ': 141, '̩ˈ': 142, '"ˈ': 143, ',ˈ': 144, 'ŋˈ': 145, 'əˌ': 146, '!ˈ': 147, '"ˌ': 148, '?ˌ': 149, ',ˌ': 150, '—ˌ': 151, '̩ˌ': 152, 'əˈ': 153, '!ˌ': 154, 'ɬ': 155, 'ʲ': 156, '¡': 157, 'ɯ': 158, 'qˌ': 159, 'ʑ': 160, 'ʑˈ': 161, '¿': 162, 'ɑːˈ': 163, 'iːː': 164, 'ɛˈ': 165, '¡ˈ': 166, 'æˈ': 167, 'ç': 168, 'ɾˌ': 169, 'ᵻˈ': 170, 'xˈ': 171, 'ɔːˈ': 172, ';': 173, 'ɬˌ': 174, ':': 175, 'ʔˈ': 176, 'ɑːˌ': 177, 'ɬˈ': 178}
|
||||
def tokenize(content, lang_marker="en"):
|
||||
split = content.split(" ")
|
||||
phones = [f"<s>"] + [ " " if not p else p for p in split ] + [f"</s>"]
|
||||
return torch.tensor([*map(symmap.get, phones)]).to()
|
||||
|
||||
# to-do: unmangle this and the resp shit
|
||||
qnt = torch.load("data/qnt.pt")[0].t()[:, :cfg.models.prom_levels].to(device)
|
||||
|
||||
text_list = [
|
||||
#torch.tensor([1, 2, 3], device=device),
|
||||
tokenize("ˈ a ɪ w ɪ l nˌ ɑː t ˈ æ s k ɐ sˈ ɛ k ə n d tˈ a ɪ m").to(device),
|
||||
]
|
||||
|
||||
proms_list = [
|
||||
x8(torch.tensor([2, 3], device=device)),
|
||||
]
|
||||
|
||||
resps_list = [
|
||||
qnt.to(device),
|
||||
]
|
||||
|
||||
kwargs = {
|
||||
'n_tokens': 1024,
|
||||
'd_model': 1024,
|
||||
'n_heads': 16,
|
||||
'n_layers': 12,
|
||||
}
|
||||
model = NAR(**kwargs).to(device)
|
||||
steps = 500
|
||||
optimizer = ml.Prodigy(model.parameters(), lr=1.0)
|
||||
engine = Engine(model=model, optimizer=optimizer)
|
||||
|
||||
def sample( name ):
|
||||
engine.eval()
|
||||
codes = engine( text_list, proms_list, resps_list=[r[..., 0].unsqueeze(-1) for r in resps_list], sampling_temperature=0.2 )
|
||||
decode_to_file( codes[0], f"data/nar.{name}.wav", device )
|
||||
|
||||
def train():
|
||||
engine.train()
|
||||
t = trange(steps)
|
||||
for i in t:
|
||||
stats = {"step": i}
|
||||
stats |= engine.traverse(text_list=text_list, proms_list=proms_list, resps_list=resps_list)
|
||||
|
||||
tqdm.write(f"{stats}")
|
||||
|
||||
sample("init")
|
||||
train()
|
||||
sample("final")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
example_usage()
|
||||
|
|
@ -109,7 +109,7 @@ if __name__ == "__main__":
|
|||
path = cfg.relpath / "logs"
|
||||
paths = path.rglob(f"./*/{args.filename}")
|
||||
|
||||
args.models = [ model for model in cfg.models.get() if model.training and (args.model == "*" or model.name in args.model) ]
|
||||
args.models = [ model for model in cfg.model.get() if model.training and (args.model == "*" or model.name in args.model) ]
|
||||
|
||||
if args.ys == "":
|
||||
args.ys = ["loss"]
|
||||
|
|
|
|||
|
|
@ -54,14 +54,13 @@ def init_tts(restart=False):
|
|||
|
||||
parser = argparse.ArgumentParser(allow_abbrev=False)
|
||||
parser.add_argument("--yaml", type=Path, default=os.environ.get('VALLE_YAML', None)) # os environ so it can be specified in a HuggingFace Space too
|
||||
parser.add_argument("--ar-ckpt", type=Path, default=None)
|
||||
parser.add_argument("--nar-ckpt", type=Path, default=None)
|
||||
parser.add_argument("--model-ckpt", type=Path, default=None)
|
||||
parser.add_argument("--device", type=str, default="cuda")
|
||||
parser.add_argument("--amp", action="store_true")
|
||||
parser.add_argument("--dtype", type=str, default="auto")
|
||||
args, unknown = parser.parse_known_args()
|
||||
|
||||
tts = TTS( config=args.yaml, ar_ckpt=args.ar_ckpt, nar_ckpt=args.nar_ckpt, device=args.device, dtype=args.dtype if args.dtype != "auto" else None, amp=args.amp )
|
||||
tts = TTS( config=args.yaml, model_ckpt=args.model_ckpt, device=args.device, dtype=args.dtype if args.dtype != "auto" else None, amp=args.amp )
|
||||
return tts
|
||||
|
||||
@gradio_wrapper(inputs=layout["inference"]["inputs"].keys())
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user